Systems and methods for generating a power consumption rating include receiving instrumentation data corresponding to a plurality of applications. The received instrumentation data is processed to calculate a relative power consumption value for each application of the plurality of applications. The relative power consumption value for each application is compared and a power consumption rating for each application based on the comparison is generated, thereby providing a visual indicator of power consumption for the applications that can be easily evaluated.

This document describes a framework for measuring and improving the performance of applications, such as distributed applications and web applications. In one aspect, a method includes performing a test on an application. The test includes executing the application on one or more computers and, while executing the application, simulating a set of workload scenarios for which performance of the application is measured during the test. While performing the test, a set of performance metrics that indicate performance of individual components involved in executing the application during the test is obtained. A knowledge graph is queried using the set of performance metrics. The knowledge graph links the individual components to corresponding performance metrics and defines a set of hotspot conditions that are each based on one or more of the corresponding performance metrics for the individual components. A given hotspot condition is detected based on the set of performance metrics.

A method, apparatus, system, and computer program code for retrieving data records. A set of static configuration objects is provided, including: a set of resources that describe available data items, and a set of views that express a serialized transformation of resources objects into a response. In response to receiving a data request, a computer system generates a data fetch execution plan from the set of resources and the set of views. The data fetch execution plan is generated using an executor adapted to a particular data store and set of performance requirements. The computer system retrieves the data records according to the data fetch execution plan.

A device may detect a trigger to perform a benchmarking task. The benchmarking task may include a first benchmarking of a first resource utilization associated with one or more tasks completed via an automated procedure. The benchmarking task may include a second benchmarking of a second resource utilization associated with the one or more tasks completed via a manual procedure. The device may determine project data relating to a project platform based on detecting the trigger to perform the benchmarking task. The device may process the project data relating to the project platform to benchmark the project. The device may generate a recommendation relating to completion of the one or more tasks using the automated procedure or the manual procedure. The device may communicate with one or more other devices to perform a response action based on the recommendation.

Provided in an embodiment of the present disclosure is a method for acquiring data of a data source associated with a production line, including: acquiring a semantic model, the semantic model including semantic relationships between respective semantic units and data source identifiers corresponding to one or more production lines; receiving production line identifiers and acquiring one or more semantic units; converting, based upon the semantic model, the one or more semantic units to data source identifiers corresponding to production lines indicated by the production line identifiers; and acquiring data of data sources indicated by the data source identifiers. Implementing the embodiments disclosed in the present disclosure simplifies a configuration file required for a virtual model of a production line, thereby greatly reducing configuration workload, and enhancing convenience of acquiring data of a data source.

Aspects of the present disclosure involve systems and methods for performing operations comprising providing a messaging application comprising a feature to a client device, the feature being implemented by operations having alternative complexity levels, wherein a first complexity level represents a first amount of device resources consumed by a first set of operations, and wherein a second complexity level represents a second amount of device resources consumed by a second set of operations; determining that the first configuration rule is satisfied by a first property of the client device; and in response to determining that the first configuration rule is satisfied by the first property of the client device, causing the feature to be implemented on the client device by the first set of operations having the first complexity level that consume a greater amount of device resources than the second set of operations having the second complexity level.

A system is configured to track and store system and event data for various computing devices. The system is configured to associate the various computing devices with profiles based at least in part on characteristics of the computing devices. The system is further configured to compare performance data and/or performance metrics for particular computing devices having a particular profile against all other devices that share the particular profile. The system then displays this comparison to a user of the particular computing device, substantially automatically diagnoses an issue with the particular computing device based on the performance and system event data, and/or enables the user to diagnose the problem based on the performance and system event data.

A system is configured to obtain information relating to a current application environment of a software application and build a plurality of model application environments based on the obtained information. The system runs the software application using the current application environment and each of the model application environments. The system collects a plurality of performance metrics related to performance of the software application in the current application environment and each of the model application environments while running in the simulated environment. The system generates a recommendation report based on the performance metrics, wherein the recommendation report comprises a recommendation of a different technology product for at least one of the technology components used in the current application environment, wherein the different technology product is different from a current technology product used for the at least one technology component in the current application environment.

A system is configured to obtain information relating to a current application environment and a plurality of model application environments of a software application. The system runs the software application using the current application environment and each of the model application environments. The system collects a plurality of performance metrics related to performance of the software application in the current application environment and each of the model application environments while running in the simulated environment. The system assigns a score to each performance metric and determines a model application environment that yielded a higher score for a performance metric as compared to the score of the performance metric in the current application environment. The system recommends at least one technology product used for a corresponding technology component associated with the performance metric in the determined model application environment.

A system is configured to obtain a plurality of performance metrics related to performance of a software application in a current application environment and each of a plurality of model application environments. The system assigns a score to each of the performance metrics collected for the current application environment and each of the model application environments, compares the respective scores assigned to each performance metric collected for the current application environment and each of the model application environments, and detects that at least one model application environment has a higher score associated with at least one performance metric as compared to the respective score of the at least one performance metric collected for the current application environment. The system determines a recommendation to use the at least one model application environment for the software application based on the detecting.